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Sense of Weight Control Prior to Pregnancy Could Help Predict Women at Risk for Excessive Gestational Weight Gain

2017· article· en· W4389022972 on OpenAlexaffabout
Laura M Adam, Mette S Madsen, Wendy Lawrence, Mary Barker, Donna Manca, Rhonda C. Bell

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPregnancyMedicineWeight gainGestationObstetricsPrenatal careGestational ageMarital statusPopulationBody weightEnvironmental healthInternal medicine

Abstract

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Introduction Gaining weight in excess of the Institute of Medicine gestational weight gain (GWG) guidelines is associated with adverse health outcomes for mother and child. It is estimated that 49% of women in Alberta gain excess gestational weight. Weight gain in pregnancy is multifactorial and there is a need to better support women during pregnancy to gain weight within the guidelines. A study in Edmonton, AB, aimed to understand if differing amounts of lifestyle support provided by a Registered Dietitian (RD) throughout pregnancy could help women achieve appropriate GWG. The purpose of these analyses is to understand if there was any difference in GWG between the three groups and to explore the gestational weight related perspectives of the women within the study. Methods Seventy low‐risk pregnant women, <20 weeks gestation, were randomized to an active control (AC) or intervention (INT) group where they saw a RD for two visits in pregnancy. The two visits occurred at < 24 weeks and 30 weeks gestation. Women completed lifestyle questionnaires throughout pregnancy and a postpartum questionnaire that asked about their prenatal experiences. A passive control group (PC) of women ≤12 months postpartum (n=55) was recruited to only complete the postpartum questionnaire. This group models standard prenatal care in Alberta (no RD visits in pregnancy). GWG information was collected from obstetrical charts and adherence to GWG recommendations was defined in accordance with IOM guidelines. Data were analyzed using chi‐square, t‐test, or one‐way ANOVA. Results Pre‐pregnancy BMI, ethnicity, education, marital status, household income and parity did not differ between INT (n=33), AC (n=37), and PC (n=55) groups. However, maternal age was different between the groups with the mean age in the PC group as 32.1 compared to 34.4 years for both the INT and AC groups (p=0.022). When asked about GWG perspectives, 78% (n=81) of women stated the amount of weight they gained was important to them with the top 3 reasons being: “To have a healthy pregnancy”, “To deliver a healthy baby” and “For my health”. Intentional changes were made by 68% of the women to help manage their weight in pregnancy, with ~45% changing the type of foods they ate (n=57) and 28% increasing their physical activity (n=35). Total GWG, rate of weight gain and adherence to GWG guidelines did not differ between the three groups. Overall, 50%, 42% and 8% of the participants exceeded, met and fell below the guidelines, respectively. Guideline concordance differed by pre‐pregnancy BMI category (p=0.024), with 78% of the overweight and 67% of the obese women exceeding the guidelines compared with 34% of women within a normal BMI. Women who agreed to feeling in control of their weight prior to pregnancy were more likely to gain weight within the guidelines during pregnancy (p=0.008). Conclusion Most women understand why weight gain in pregnancy is important, yet half exceed the guidelines. Understanding a woman's sense of weight control prior to pregnancy may help aid healthcare providers understand which women who may be at increased risk for excess GWG. Support or Funding Information The Danone Institute of Canada, Alberta Innovates Health Solutions

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.295
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes2
Has abstractyes

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